
We propose Adaptive Graph Temporal Convolution Network (AdaGTCN), a novel neural architecture composed of - Adaptive Graph Learning layer, Deep Neighborhood Graph Convolution layers and Dilated Inception Layers, all combined sequentially for identifying reading activities from synchronous EEG and eye-tracking data provided by ZuCo \(2.0\)  corpus [1]}.

Our proposed Adaptive Graph Learning Layer utilizes the spatio-temporal relationship between the EEG electrodes to dynamically learn the graph network structure based on EEG sequences during word-level eye-movement fixations and the Deep Neighborhood Graph Convolution Layers interleaved with Dilated Inception Layers simultaneously exploit the message passing in interdependent EEG electrodes while preserving their short range temporality.

